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模拟式感知人形运动

原文标题 · Perceptive Humanoid Motion from Simulation

作者: Siyuan Hu, Abulikemu Abuduweili, Yue Meng | 人形机器人有潜力实现多种物理任务的自动化。然而,迈向通用人形机器人自主性的进展仍受数据瓶颈限制:将像素和自由形式语言映射为低级关节指令需要配套监督,且目前尚未存在足够大规模的此类数据语料库。某些方法依赖于人类远程操作与可穿戴捕捉 d

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Computer Science > Robotics

Title: Perceptive Humanoid Motion from Simulation

Abstract: Humanoid robots have the potential to automate a wide range of physical tasks. Progress toward generalist humanoid autonomy, however, remains constrained by a data bottleneck: mapping pixels and free-form language to low-level joint commands requires paired supervision, and no corpus of such data exists at sufficiently large scale. Some approaches rely on human teleoperation and wearable capture devices, which require at least an hour of human time to produce an hour of data. We present HALO, a humanoid autonomy system powered by a pipeline that synthesizes vision-language-motion data across a thousand procedurally generated homes. The corpus scales with compute rather than human labor. HALO acquires a repertoire of physical skills, including sitting on chairs, navigating to named objects, stepping sideways through half-open doors, crouching and crawling beneath tables, and pushing boxes. It orchestrates these skills to solve composite, long-horizon tasks. Without ever seeing a real home during training, HALO transfers zero-shot to the physical world, achieving a 91% success rate across 360 trials in six unmapped real-world environments. See this https URL for code and videos.

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